This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Community Hub.
Last week, while working on a support case, our customer was facing performance issues in their Python application.
After some investigation, I decided to suggest CProfiler to identify which function call was taking the most time and use that as a starting point for troubleshooting.
So profiling the Python code became essential to pinpoint the bottleneck. I suggested using CProfiler, a built-in Python module, which helps you profile your code and identify performance issues in real time.
But first, I ran some examples with my own test code to see the results.
In this code snippet:
- RunCommandTimeout is a function that performs command timeouts against a SQL database.
- cProfile.Profile() starts profiling the code within the context.
- After execution, pstats.Stats(Profile) helps us visualize the most time-consuming calls, sorting them by cumulative time.
I was able to track the exact function calls and time spent on each one, for example,
- Function calls and their frequency
- Time spent in each function (including sub-calls)
- Cumulative time taken by functions, which is useful for spotting bottlenecks
Analyzing the profiling results for the Python code execution, sorted by cumulative time, I was able to find the following results:
- I found very interesting points, such as the execute method being called 7 times with a total time of 54 seconds. This indicates that the execute method is responsible for the majority of the time spent in this execution. Each call takes an average of 7.73 seconds, suggesting that the database queries are the primary bottleneck.
- Additionally, my time.sleep function shows that it was called to introduce delays, likely as part of the retry mechanism built into the RunCommandTimeout function. Each sleep call lasted for an average of 9 seconds.
Enjoy!